Robust identification of topological phase transition by self-supervised machine learning approach

被引:6
作者
Ho, Chi-Ting [1 ,2 ]
Wang, Daw-Wei [1 ,2 ,3 ,4 ]
机构
[1] Natl Ctr Theoret Sci, Phys Div, Hsinchu 30013, Taiwan
[2] Natl Tsing Hua Univ, Phys Dept, Hsinchu 30013, Taiwan
[3] Natl Tsing Hua Univ, Ctr Theory & Computat, Hsinchu 30013, Taiwan
[4] Natl Tsing Hua Univ, Ctr Quantum Technol, Hsinchu 30013, Taiwan
关键词
machine learning; topological phase transition; self-supervised learning; time-of-flight experiments; QUANTUM; REALIZATION; FERMIONS; POINTS; PARITY; MATTER; MODEL;
D O I
10.1088/1367-2630/ac1709
中图分类号
O4 [物理学];
学科分类号
0702 ;
摘要
We propose a systematic methodology to identify the topological phase transition through a self-supervised machine learning model, which is trained to correlate system parameters to the non-local observables in time-of-flight experiments of ultracold atoms. Different from the conventional supervised learning approach, where the predicted phase transition point is very sensitive to the training region and data labeling, our self-supervised learning approach identifies the phase transition point by the largest deviation of the predicted results from the known system parameters and by the highest confidence through a systematic shift of the training regions. We demonstrate the robust application of this approach results in various 1D and 2D exactly solvable models, using different input features (time-of-flight images, spatial correlation function or density-density correlation function). As a result, our self-supervised approach should be a very general and reliable method for many condensed matter or solid state systems to observe new states of matters solely based on experimental measurements, even without a priori knowledge of the phase transition models.
引用
收藏
页数:14
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